Genetic Programming Theory and Practice XI [electronic resource] / edited by Rick Riolo, Jason H. Moore, Mark Kotanchek.

Extreme Accuracy in Symbolic Regression -- Exploring Interestingness in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer's Disease -- Optimizing a Cloud Contract Portfolio using Genetic Programming-based Load Models -- Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data -- Grounded Simulation: Using Simulated Evolution to Guide Embodied Evolution -- Applying Genetic Programming in Business Forecasting -- Explaining Unemployment Rates with Symbolic Regression -- Uniform Linear Transformation with Repair and Alternation in Genetic Programming -- A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data -- Gaining Deeper Insights in Symbolic Regression -- Geometric Semantic Genetic Programming for Real Life Applications -- Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud – communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3) The need for model analysis workflows for insight generation based on generated GP solutions – model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combining different types of data. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.